• Journal of Information Processing Systems
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• v.8 no.4
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• pp.653-668
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• 2012

In this study, an improved HMM based recognition model is proposed for online English and Korean handwritten characters. The pattern elements of the handwriting model are sub character strokes and ligatures. To deal with the problem of handwriting style variations, a modified Hierarchical Clustering approach is introduced to partition different writing styles into several classes. For each of the English letters and each primitive grapheme in Korean characters, one HMM that models the temporal and spatial variability of the handwriting is constructed based on each class. Then the HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwritten characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of the HMM based method, a post-processing procedure that takes the global and structural features into account is proposed. Experiments showed that the proposed recognition system achieved a high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition was also performed to evaluate the system.

• The Journal of Korean society of community based occupational therapy
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• v.4 no.2
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• pp.63-73
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• 2014

Objective : The purpose of this study was to identify the effect of crafts and handwriting on bilateral coordination during task performance. Methods : Randomly selected 30 college students without hand disability were invited for the study, and grouped 3(test group 1 for crafts, test group 2 for handwriting, and control group) with 10 students per group respectively. Then Jebsen-taylor hand function test, Purdue pegboard test, and Minnesota manual dexterity test were employed for evaluating changes before and after the intervention. Results : After training intervention of crafts and handwriting for two test groups, test groups showed better bilateral coordination significantly than the control group. Especially test group 1(crafts) showed a bigger difference at Jebsen-taylor hand function test, and likely test group 2(handwriting) did at Purdue pegboard test. Conclusion : It was found that crafts increase bilateral coordination, while handwriting increase hand dexterity during task performance. That is, crafts and handwriting affect tasks differently. Further studies applying various crafts and handwriting for many age groups will be helpful for identifying the better way of occupational intervention for individuals in lack of bilateral coordination.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Analytical Science and Technology
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• v.23 no.2
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• pp.205-215
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• 2010

The purpose of this study is to examine Hangul signature changes of writers under the influence of alcohol using Scale for Handwriting Identification (SHWI). It has been recognized that handwriting is influenced by alcohol. However, in Korea, there has been no study which examined the handwriting changes after drinking alcohol. This study confirmed the differences between signatures in sobriety at police station (SIS) and signatures under the influence of alcohol at sobriety checkpoint (S-UIA) by analyzing the Hangul signature of 30 persons. The comparative characteristics are size, space, omission, writing order, connection. The changes of more than one characteristic were observed among the 27 out of 30 persons. Three of 30 persons did not show any change between S-IS and S-UIA.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.63-68
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• 2010

Recently, to accelerate the Ubiquitous generation, the input interface of the mobile machinery and tools are actively being researched. In addition with the existing interfaces such as the keyboard and curser (mouse), other subdivisions including the handwriting, voice, vision, and touch are under research for new interfaces. Especially in the case of small-sized mobile machinery and tools, there is a increasing need for an efficient input interface despite the small screens. This is because, additional installment of other devices are strictly limited due to its size. Previous studies on handwriting recognition have generally been based on either two-dimensional images or algorithms which identify handwritten data inserted through vectors. Futhermore, previous studies have only focused on how to enhance the accuracy of the handwriting recognition algorithms. However, a problem arisen is that when an actual handwriting is inserted, the user must select the classification of their characters (e.g Upper or lower case English, Hangul - Korean alphabet, numbers). To solve the given problem, the current study presents a system which distinguishes different languages by analyzing the form/shape of inserted handwritten characters. The proposed technique has treated the handwritten data as sets of vector units. By analyzing the correlation and directivity of each vector units, a more efficient language distinguishing system has been made possible.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.384-388
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• 2005

Currently most of the online handwriting recongition system are using free style input method. However, it has disadvantages of ill-recongition. In this paper, we present a new online Korean HAndwriting recongition system(KOHA) which give a slice restriction and remove the ill-recongition. KOHA uses boundary lines of input window and the stenography is possible with KOHA. Also, KOHA has the advantage of Unistroke.

• Korean Journal of Occupational Therapy
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• v.26 no.4
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• pp.43-55
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• 2018

Objective : The purpose of this study was to investigate the effects of fidget spinner training on the hand function and handwriting legibility of lower grade elementary school studens. Methods : This study randomly assigned a study group of 12 children and control group of 12 children from 24 children in grade 1 and 2 (ages 7 through 8), whose are dominantly right handed. The study used was a pre-post process. The intervention was conducted only on the study group twice a week for 5 weeks and for 40 minutes per session, for a total of ten sessions. The measuring instruments used to compare the hand functions and handwriting legibility were the Jebsen-Taylor Hand Function Test, Grip Strength Test, and Legibility Test. The data analysis used a Wilcoxon signed rank, Mann-Whitney U and Chi-Square cross analysis. Results : The fidget spinner training showed significant improvement in the study group's hand function(grip strength and handwriting legibility) and a significant difference was shown between the control and study groups. Conclusion : This study confirmed the value and utility of a fidget spinner as a tool for improving the hand function and handwriting legibility of elementary school students in lower grades. Future studies are expected to verify the effectiveness of the fidget spinner training based on the present study.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.13-22
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• 2024

Currently, with the proliferation of digital devices, the significance of handwritten texts in daily lives is gradually diminishing. As the use of keyboards and touch screens increase, a decline in Korean handwriting quality is being observed across a broad spectrum of Korean documents, from young students to adults. However, Korean handwriting still remains necessary for many documentations, as it retains individual unique features while ensuring readability. To this end, this paper aims to implement an application designed to improve and correct the quality of handwritten Korean script The implemented application utilizes the CRAFT (Character-Region Awareness For Text Detection) model for handwriting area detection and employs the VGG-Feature-Extraction as a deep learning model for learning features of the handwritten script. Simultaneously, the application presents the user's handwritten Korean script's reliability on a syllable-by-syllable basis as a recognition rate and also suggests the most similar fonts among candidate fonts. Furthermore, through various experiments, it can be confirmed that the proposed application provides an excellent recognition rate comparable to conventional commercial character recognition OCR systems.

• Journal of Korean Critical Care Nursing
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• v.9 no.1
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• pp.1-14
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• 2016

Purpose: To investigate the post-intensive care syndrome (PICS) and to analyze the factors affecting the quality of life (QoL) of survivors of critical illness. Methods: Subjects were 114 outpatients who had been discharged from intensive care units of a university hospital in B city, Korea. From July 30 through September 30, 2015, PICS was assessed using the Korean Montreal Cognitive Assessment, Hospital Anxiety-Depression Scale, Korean Instrumental/Activities of Daily Living (K-I/ADL) index, and handwriting transformation, while physical and mental health-related QoL was measured using the SF-12. Results: Of the subjects, 39.5% were screened for mild cognitive disorder and 23.7% experienced handwriting transformation after discharge. Multiple regression analysis revealed that restraint application, current job, time of ${\geq}36$ months after discharge, depression, anxiety, and handwriting transformation accounted for 40.9% of the physical health-related QoL, and depression, anxiety and experience of delirium accounted for 62.4% of the mental health-related QoL. Conclusions: It is necessary to make efforts to reduce restraint application in intensive care units and prevent the occurrence of delirium, with the objective of reducing PICS and improving the QoL of critical illness survivors.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.316-321
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• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.